1 Run NGSadmix Steps

1.2 Run NGSadmix

# run k=2-5 (run_NGSadmix.sh)
for i in {2..5} 
do 
NGSadmix -likes /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_maf05_pruned.BEAGLE.PL.gz -K $i -o /home/ktist/ph/data/NGSadmix/Ph_pruned_maf05_k$i -P 8 
done

1.3 Run evalAdmix (locally)

#evalAdmix_runlocal.sh

evalAdmix -beagle Data/ngsadmix/PH_maf05_pruned_BEAGLE.PL.gz -fname Data/ngsadmix/PH_pruned_maf05_k2.fopt.gz -qname Data/ngsadmix/PH_pruned_maf05_k2.qopt -P 8 -o output.corres.k2.txt

2 Results from NGSadmix

source("../Rscripts/BaseScripts.R")
source("../Rscripts/visFuns.R")

library(tidyverse)
library(tibble)

#Output files
qfiles<-list.files("../Data/ngsadmix/",pattern=".qopt")

ofiles<-list.files("../Data/ngsadmix/",pattern="output.corres")

#population info
pop<-read.csv("../Data/Sample_metadata_892pops.csv")

pop$Population.Year<-factor(pop$Population.Year, levels=c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17","BC17","WA17","CA17"))

poporder<-paste(pop$Population.Year[order(pop$Population.Year)])
pop_order<-c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17","BC17","WA17","CA17")

#color for populations
#levels=c("TB","PWS","SS", "BC","WA","CA"))
#colors= cols ("#56b4e9" "#cc79a7" "#009e73" "#0072b2" "#d55e00" "#e69f00" "#f0e442")

for (i in 1:length(qfiles)){
    # extract K from the file name
    oname<-ofiles[i]
    k<-as.integer(substr(oname, 16,17))
    
    #read the qopt file for k=k
    q<-read.table(paste0("../Data/ngsadmix/", qfiles[i]))
    
    #order according to population and plot the NGSadmix results
    q$id<-pop$Population.Year
    q<-q[order(q$id),]
    
    ord<-orderInds(pop = as.vector(poporder), q = q[,1:(i+1)])
    
    xlabels<-data.frame(x=tapply(1:length(poporder),list(poporder), mean))
    xlabels$pop<-factor(rownames(xlabels), levels=pop_order)
    xlabels<-xlabels[order(xlabels$pop),]
    
    # c('PWS color', 'TB color', 'CA color" )
    #if (i==1|i==2) colors=c(4,2,7)
    if (i==1|i==2) colors=c(2,4,7)
    #colors<-c("#cc79a7","#56b4e9", "#e69f00")
    #if (i==3) colors=c(5,4,7,2,3) 
    if (i==3) colors=c(5,2,7,4,3) 
    if (i==4) colors=c(4,2,5,7,3)

    {png(paste0("../Output/ngsadmix/Admix_plot_k",k,".png"), height = 3.5, width=8, unit="in", res=300)
    barplot(t(q[,1:(i+2)])[,ord],col=colors,space=0,border=NA,xaxt="n",xlab="Population",ylab=paste0("Admixture proportions for K=",k))
    text(xlabels$x,-0.05,xlabels$pop,xpd=T, srt=90, adj=1,cex=0.8)
    abline(v=cumsum(sapply(unique(poporder[ord]),function(x){sum(pop[ord,"Population.Year"]==x)})),col=1,lwd=1.2)
    dev.off()}
    
    #Plot the correlation matrix from evalAdmix
    r<-read.table(paste0("../Data/ngsadmix/",ofiles[i]))
    
    # Plot correlation of residuals
    {pdf(paste0("../Output/ngsadmix/evalAdmix_corplot_k",k,".pdf"), height = 8, width=10)
    plotCorRes(cor_mat = r, pop = as.vector(pop[,"Population.Year"]), ord = ord, title=paste0("Evaluation of admixture proportions with K=",k), max_z=0.1, min_z=-0.1)
    dev.off()}
}

  • Correlation of residuals


3 Use Clumpak to choose the best K

3.1 Run NGSadmix 10 times for each K (K= 3 & 4)

  • see RunNGSadmix.sh

3.2 Compile all likelihood numbers from log files into 1 (logfile)

#linux code (won't work for unix)
(for log in `ls *.log`; do grep -oP 'Ph_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > logfile_k3
(for log in `ls *.log`; do grep -oP 'Ph_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > logfile_k4

3.3 Read ‘logfile’ & create an input file for Clumpak

log2<-read.table("../Data/ngsadmix/logfile_k2", sep="\t", header =FALSE)
log3<-read.table("../Data/ngsadmix/logfile_k3", sep="\t", header =FALSE)
log4<-read.table("../Data/ngsadmix/logfile_k4", sep="\t", header =FALSE)
logk2<-log2[c(FALSE,TRUE),]
logk3<-log3[c(FALSE,TRUE),]
logk4<-log4[c(FALSE,TRUE),]

logs<-data.frame(K=c(rep(2, times=10),rep(3, times=10), rep(4, times=10)), Liklihood=c(logk2,logk3, logk4))
write.table(logs, "../Output/ngsadmix/logs.txt", sep="\t", row.names = F, col.names = F, quote=F)
# DO NOT use special character in the file name
#Must have at least three K values

#upload the logs.txt to Clumpak website
# http://clumpak.tau.ac.il

#'Estimating the Best K (from Clumpak)'

# The method of Evanno enables the user to identify a single k value, out of a range of K values, which captures the uppermost 
# level of structure. This method was purposed by Evonno et al. in 2005 (Molecular Ecology 14, 2005). In addition, we also use
# ln(Pr(X|K) #values in order to identify the k for which Pr(K=k) is highest (as described in STRUCTURE's manual, section 5.1).

#STRUCTURE manual
#' it is not infrequent that in real data the value of our model choice criterion continues to increase with increasing K. 
# Then it usually makes sense to focus on values of K that capture most of the structure in the data and that seem 
# biologically sensible.'


# plot admix values
  • Resuls from CLUMPAK
    “Optimal K by Evanno is: 3”
---
title: "NGSAdmix"
output": html_notebook
output:
  html_notebook:
      toc: true 
      toc_float: true
      number_sections: true
      theme: lumen
      highlight: tango
      code_folding: hide
      df_print: paged
---
# Run NGSadmix Steps

## Prune SNPs: Using Plink to prune highly linked snps  

* Plink output file: PH_maf05_prune_50.5.0.5.prune.in
    - Need to reformat the plink output (xxx.prune.in) file to a bed format in order to subset a vcf file  
```{bash eval=FALSE, echo=TRUE}
#Reformat prun.in file with running the reformat_prunin.R
R
source("reformat_prunin.R")
reformat_prunin("ph/data/new_vcf/MD7000/pruned/PH_maf05_prune_50.5.0.5.prune.in")
#output ph/data/new_vcf/MD7000/pruned/PH_maf05_prune_50.5.0.5.prune.in.sites.txt
quit()

## Use bcftools to subset the VCF file with .prune.in.sutes.txt
bcftools view -R /home/ktist/ph/data/new_vcf/MD7000/pruned/PH_maf05_prune_50.5.0.5.prune.in.sites.txt  /home/ktist/ph/data/new_vcf/MD7000/PH_DP600_7000_minQ20_minMQ30_NS0.5_maf05.vcf.gz > /home/ktist/ph/data/new_vcf/MD7000/pruned/PH_DP600_7000_NS0.5_maf05_pruned.vcf
bgzip /home/ktist/ph/data/new_vcf/MD7000/pruned/PH_DP600_7000_NS0.5_maf05_pruned.vcf

#Create beagle files (create_beagle.sh)
#e.g. do chromosome by chromosome
vcftools --gzvcf /home/ktist/ph/data/new_vcf/MD7000/pruned/PH_DP600_7000_NS0.5_maf05_pruned.vcf.gz --chr chr1 --out /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_pruned_c1 --BEAGLE-PL 

# remove the head from chr2-26 (slurm scripts do not work, so run the command on the terminal) (combined_beagle.sh)
sed -e '1, 1d' < /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_pruned_c2.BEAGLE.PL  > /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c2.BEAGLE.PL 

#Combined all into one file
cat /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_pruned_c1.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c2.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c3.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c4.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c5.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c6.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c7.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c8.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c9.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c10.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c11.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c12.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c13.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c14.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c15.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c16.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c17.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c18.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c19.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c20.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c21.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c22.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c23.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c24.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c25.BEAGLE.PL /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_noheader_c26.BEAGLE.PL  > /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_maf05_pruned_BEAGLE.PL 

bgzip /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_maf05_pruned_BEAGLE.PL 

```

## Run NGSadmix  

```{bash}
# run k=2-5 (run_NGSadmix.sh)
for i in {2..5} 
do 
NGSadmix -likes /home/ktist/ph/data/new_vcf/MD7000/beagle/PH_maf05_pruned.BEAGLE.PL.gz -K $i -o /home/ktist/ph/data/NGSadmix/Ph_pruned_maf05_k$i -P 8 
done 

```


## Run evalAdmix (locally)
```{bash}
#evalAdmix_runlocal.sh

evalAdmix -beagle Data/ngsadmix/PH_maf05_pruned_BEAGLE.PL.gz -fname Data/ngsadmix/PH_pruned_maf05_k2.fopt.gz -qname Data/ngsadmix/PH_pruned_maf05_k2.qopt -P 8 -o output.corres.k2.txt

```


# Results from NGSadmix

```{r eval=FALSE, message=FALSE, warning=FALSE}
source("../Rscripts/BaseScripts.R")
source("../Rscripts/visFuns.R")

library(tidyverse)
library(tibble)

#Output files
qfiles<-list.files("../Data/ngsadmix/",pattern=".qopt")

ofiles<-list.files("../Data/ngsadmix/",pattern="output.corres")

#population info
pop<-read.csv("../Data/Sample_metadata_892pops.csv")

pop$Population.Year<-factor(pop$Population.Year, levels=c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17","BC17","WA17","CA17"))

poporder<-paste(pop$Population.Year[order(pop$Population.Year)])
pop_order<-c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17","BC17","WA17","CA17")

#color for populations
#levels=c("TB","PWS","SS", "BC","WA","CA"))
#colors= cols ("#56b4e9" "#cc79a7" "#009e73" "#0072b2" "#d55e00" "#e69f00" "#f0e442")

for (i in 1:length(qfiles)){
    # extract K from the file name
    oname<-ofiles[i]
    k<-as.integer(substr(oname, 16,17))
    
    #read the qopt file for k=k
    q<-read.table(paste0("../Data/ngsadmix/", qfiles[i]))
    
    #order according to population and plot the NGSadmix results
    q$id<-pop$Population.Year
    q<-q[order(q$id),]
    
    ord<-orderInds(pop = as.vector(poporder), q = q[,1:(i+1)])
    
    xlabels<-data.frame(x=tapply(1:length(poporder),list(poporder), mean))
    xlabels$pop<-factor(rownames(xlabels), levels=pop_order)
    xlabels<-xlabels[order(xlabels$pop),]
    
    # c('PWS color', 'TB color', 'CA color" )
    #if (i==1|i==2) colors=c(4,2,7)
    if (i==1|i==2) colors=c(2,4,7)
    #colors<-c("#cc79a7","#56b4e9", "#e69f00")
    #if (i==3) colors=c(5,4,7,2,3) 
    if (i==3) colors=c(5,2,7,4,3) 
    if (i==4) colors=c(4,2,5,7,3)

    {png(paste0("../Output/ngsadmix/Admix_plot_k",k,".png"), height = 3.5, width=8, unit="in", res=300)
    barplot(t(q[,1:(i+2)])[,ord],col=colors,space=0,border=NA,xaxt="n",xlab="Population",ylab=paste0("Admixture proportions for K=",k))
    text(xlabels$x,-0.05,xlabels$pop,xpd=T, srt=90, adj=1,cex=0.8)
    abline(v=cumsum(sapply(unique(poporder[ord]),function(x){sum(pop[ord,"Population.Year"]==x)})),col=1,lwd=1.2)
    dev.off()}
    
    #Plot the correlation matrix from evalAdmix
    r<-read.table(paste0("../Data/ngsadmix/",ofiles[i]))
    
    # Plot correlation of residuals
    {pdf(paste0("../Output/ngsadmix/evalAdmix_corplot_k",k,".pdf"), height = 8, width=10)
    plotCorRes(cor_mat = r, pop = as.vector(pop[,"Population.Year"]), ord = ord, title=paste0("Evaluation of admixture proportions with K=",k), max_z=0.1, min_z=-0.1)
    dev.off()}
}


```

![](../Output/ngsadmix/Admix_plot_k2_small.png)

* Correlation of residuals

![](../Output/ngsadmix/evalAdmix_corplot_k2.png)



![](../Output/ngsadmix/Admix_plot_k3_small.png)

![](../Output/ngsadmix/evalAdmix_corplot_k3.png)


![](../Output/ngsadmix/Admix_plot_k4_small.png)

![](../Output/ngsadmix/evalAdmix_corplot_k4.png)

<br>

# Use Clumpak to choose the best K
* see https://github.com/alexkrohn/AmargosaVoleTutorials/blob/master/ngsAdmix_tutorial.md

## Run NGSadmix 10 times for each K (K= 3 & 4)
* see RunNGSadmix.sh

## Compile all likelihood numbers from log files into 1 (logfile)

```{bash eval=FALSE}
#linux code (won't work for unix)
(for log in `ls *.log`; do grep -oP 'Ph_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > logfile_k3
(for log in `ls *.log`; do grep -oP 'Ph_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > logfile_k4
```


## Read 'logfile' & create an input file for Clumpak
* http://clumpak.tau.ac.il/bestK.html  
* Upload the log probability table file to the website and submit the form

```{r eval=FALSE, message=FALSE, warning=FALSE}
log2<-read.table("../Data/ngsadmix/logfile_k2", sep="\t", header =FALSE)
log3<-read.table("../Data/ngsadmix/logfile_k3", sep="\t", header =FALSE)
log4<-read.table("../Data/ngsadmix/logfile_k4", sep="\t", header =FALSE)
logk2<-log2[c(FALSE,TRUE),]
logk3<-log3[c(FALSE,TRUE),]
logk4<-log4[c(FALSE,TRUE),]

logs<-data.frame(K=c(rep(2, times=10),rep(3, times=10), rep(4, times=10)), Liklihood=c(logk2,logk3, logk4))
write.table(logs, "../Output/ngsadmix/logs.txt", sep="\t", row.names = F, col.names = F, quote=F)
# DO NOT use special character in the file name
#Must have at least three K values

#upload the logs.txt to Clumpak website
# http://clumpak.tau.ac.il

#'Estimating the Best K (from Clumpak)'

# The method of Evanno enables the user to identify a single k value, out of a range of K values, which captures the uppermost 
# level of structure. This method was purposed by Evonno et al. in 2005 (Molecular Ecology 14, 2005). In addition, we also use
# ln(Pr(X|K) #values in order to identify the k for which Pr(K=k) is highest (as described in STRUCTURE's manual, section 5.1).

#STRUCTURE manual
#' it is not infrequent that in real data the value of our model choice criterion continues to increase with increasing K. 
# Then it usually makes sense to focus on values of K that capture most of the structure in the data and that seem 
# biologically sensible.'


# plot admix values



```

* Resuls from CLUMPAK   
**"Optimal K by Evanno is: 3 "**

